A Novel Approach to Collect and Analyze Market Customer Behavior Data on Online Shop

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI:10.1109/ICITech50181.2021.9590161

Ramos Somya, E. Winarko, Sigit Privanta

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引用次数: 1

Abstract

Online shopping activities are currently experiencing a significant increase due to the development of the reach of the internet services and the changed activities from offline to online. The data analysis generated from online shopping activities is necessary to determine the right sales strategy. One of the types of data that needs to be analyzed is market consumer behavior data in online shops, generated in the form of clickstream data. Currently, there was not any research that examines the determination of clickstream data components, clickstream data recording mechanism, and how to analyze clickstream data components in online shops properly. This paper describes the architecture of the proposed online shop application, the module to record the clickstream data, and the method to analyze the clickstream data. Based on our evaluation, eight clickstream data components were successfully recorded in the database using Asynchronous JavaScript and XML (AJAX) technology. The clickstream data component that contains market customer behavior data is Multi-Criteria Decision Making (MCDM) data and has been analyzed using the Simple Additive Weighting (SAW) ranking method.

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一种收集和分析网上商店市场顾客行为数据的新方法

由于互联网服务范围的发展和活动从线下到线上的转变，网上购物活动正在显著增加。从网上购物活动中产生的数据分析对于确定正确的销售策略是必要的。需要分析的数据类型之一是在线商店中的市场消费者行为数据，以点击流数据的形式生成。目前还没有研究对点击流数据成分的确定，点击流数据记录机制，以及如何正确分析在线商店的点击流数据成分。本文介绍了所提出的网上商店应用程序的体系结构、点击流数据的记录模块以及点击流数据的分析方法。根据我们的评估，使用异步JavaScript和XML (AJAX)技术成功地将8个点击流数据组件记录在数据库中。包含市场客户行为数据的点击流数据组件是多准则决策(MCDM)数据，并使用简单加性加权(SAW)排序方法进行分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)

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